


import tensorflow as tf

from ...tf_utils import shape_list
class 					SCREAMING_SNAKE_CASE__    (							tf.keras.layers.Layer     ):
      def __init__(			self							:			Optional[Any]	,				SCREAMING_SNAKE_CASE__							:			List[Any]	,				SCREAMING_SNAKE_CASE__							:			str	,				SCREAMING_SNAKE_CASE__							:			Union[str, Any]	,				SCREAMING_SNAKE_CASE__							:			Any	,				SCREAMING_SNAKE_CASE__							:			str=1	,				SCREAMING_SNAKE_CASE__							:			Tuple=False	,				**SCREAMING_SNAKE_CASE__							:			int	)						->     Optional[Any]:
             super().__init__(**UpperCAmelCase_	)

             a_   :						Optional[int]				=    vocab_size
             a_   :						Optional[Any]				=    d_embed
             a_   :						Tuple				=    d_proj

             a_   :						Optional[int]				=    cutoffs + [vocab_size]
             a_   :						List[str]				=    [0] + self.cutoffs
             a_   :						List[str]				=    div_val

             a_   :						str				=    self.cutoffs[0]
             a_   :						Optional[int]				=    len(self.cutoffs	) - 1
             a_   :						Tuple				=    self.shortlist_size + self.n_clusters
             a_   :						Tuple				=    keep_order

             a_   :						List[str]				=    []
             a_   :						Optional[Any]				=    []
      def 			SCREAMING_SNAKE_CASE				(			self							:			List[Any]	,				SCREAMING_SNAKE_CASE__							:			List[Any]	)						->     Optional[int]:
             if self.n_clusters > 0:
                    a_   :						List[str]				=    self.add_weight(
                        shape=(self.n_clusters, self.d_embed)	,				initializer='zeros'	,				trainable=UpperCAmelCase_	,				name='cluster_weight'	)
                    a_   :						Any				=    self.add_weight(
                        shape=(self.n_clusters,)	,				initializer='zeros'	,				trainable=UpperCAmelCase_	,				name='cluster_bias'	)

             if self.div_val == 1:
                    for i in range(len(self.cutoffs	)	):
                           if self.d_proj != self.d_embed:
                                  a_   :						int				=    self.add_weight(
                                      shape=(self.d_embed, self.d_proj)	,				initializer='zeros'	,				trainable=UpperCAmelCase_	,				name=F"""out_projs_._{i}"""	,				)
                                  self.out_projs.append(UpperCAmelCase_	)
                           else:
                                  self.out_projs.append(UpperCAmelCase_	)
                           a_   :						Any				=    self.add_weight(
                               shape=(self.vocab_size, self.d_embed)	,				initializer='zeros'	,				trainable=UpperCAmelCase_	,				name=F"""out_layers_._{i}_._weight"""	,				)
                           a_   :						List[Any]				=    self.add_weight(
                               shape=(self.vocab_size,)	,				initializer='zeros'	,				trainable=UpperCAmelCase_	,				name=F"""out_layers_._{i}_._bias"""	,				)
                           self.out_layers.append((weight, bias)	)
             else:
                    for i in range(len(self.cutoffs	)	):
                           a_				,						a_   :						Tuple				=    self.cutoff_ends[i], self.cutoff_ends[i + 1]
                           a_   :						int				=    self.d_embed // (self.div_val**i)

                           a_   :						int				=    self.add_weight(
                               shape=(d_emb_i, self.d_proj)	,				initializer='zeros'	,				trainable=UpperCAmelCase_	,				name=F"""out_projs_._{i}"""	)
                           self.out_projs.append(UpperCAmelCase_	)
                           a_   :						int				=    self.add_weight(
                               shape=(r_idx - l_idx, d_emb_i)	,				initializer='zeros'	,				trainable=UpperCAmelCase_	,				name=F"""out_layers_._{i}_._weight"""	,				)
                           a_   :						List[str]				=    self.add_weight(
                               shape=(r_idx - l_idx,)	,				initializer='zeros'	,				trainable=UpperCAmelCase_	,				name=F"""out_layers_._{i}_._bias"""	,				)
                           self.out_layers.append((weight, bias)	)
             super().build(UpperCAmelCase_	)
      @staticmethod
      def 			SCREAMING_SNAKE_CASE				(			SCREAMING_SNAKE_CASE__							:			List[Any]	,				SCREAMING_SNAKE_CASE__							:			Optional[Any]	,				SCREAMING_SNAKE_CASE__							:			Optional[int]	,				SCREAMING_SNAKE_CASE__							:			Union[str, Any]=None	)						->     Union[str, Any]:
             a_   :						Dict				=    x
             if proj is not None:
                    a_   :						List[str]				=    tf.einsum('ibd,ed->ibe'	,				UpperCAmelCase_	,				UpperCAmelCase_	)
             return tf.einsum('ibd,nd->ibn'	,				UpperCAmelCase_	,				UpperCAmelCase_	) + b
      @staticmethod
      def 			SCREAMING_SNAKE_CASE				(			SCREAMING_SNAKE_CASE__							:			int	,				SCREAMING_SNAKE_CASE__							:			List[str]	)						->     Union[str, Any]:
             a_   :						Union[str, Any]				=    shape_list(UpperCAmelCase_	)
             a_   :						Dict				=    tf.range(lp_size[0]	,				dtype=target.dtype	)
             a_   :						Tuple				=    tf.stack([r, target]	,				1	)
             return tf.gather_nd(UpperCAmelCase_	,				UpperCAmelCase_	)

      def 			SCREAMING_SNAKE_CASE				(			self							:			List[Any]	,				SCREAMING_SNAKE_CASE__							:			str	,				SCREAMING_SNAKE_CASE__							:			List[str]	,				SCREAMING_SNAKE_CASE__							:			Union[str, Any]=True	,				SCREAMING_SNAKE_CASE__							:			List[str]=False	)						->     Dict:
             a_   :						Dict				=    0
             if self.n_clusters == 0:
                    a_   :						str				=    self._logit(UpperCAmelCase_	,				self.out_layers[0][0]	,				self.out_layers[0][1]	,				self.out_projs[0]	)
                    if target is not None:
                           a_   :						List[Any]				=    tf.nn.sparse_softmax_cross_entropy_with_logits(labels=UpperCAmelCase_	,				logits=UpperCAmelCase_	)
                    a_   :						Union[str, Any]				=    tf.nn.log_softmax(UpperCAmelCase_	,				axis=-1	)
             else:
                    a_   :						Union[str, Any]				=    shape_list(UpperCAmelCase_	)
                    a_   :						Union[str, Any]				=    []
                    a_   :						Optional[Any]				=    tf.zeros(hidden_sizes[:2]	)
                    for i in range(len(self.cutoffs	)	):
                           a_				,						a_   :						str				=    self.cutoff_ends[i], self.cutoff_ends[i + 1]
                           if target is not None:
                                  a_   :						Optional[int]				=    (target >= l_idx) & (target < r_idx)
                                  a_   :						Dict				=    tf.where(UpperCAmelCase_	)
                                  a_   :						str				=    tf.boolean_mask(UpperCAmelCase_	,				UpperCAmelCase_	) - l_idx

                           if self.div_val == 1:
                                  a_   :						Optional[Any]				=    self.out_layers[0][0][l_idx:r_idx]
                                  a_   :						Optional[int]				=    self.out_layers[0][1][l_idx:r_idx]
                           else:
                                  a_   :						Any				=    self.out_layers[i][0]
                                  a_   :						Optional[int]				=    self.out_layers[i][1]

                           if i == 0:
                                  a_   :						int				=    tf.concat([cur_W, self.cluster_weight]	,				0	)
                                  a_   :						Optional[Any]				=    tf.concat([cur_b, self.cluster_bias]	,				0	)

                                  a_   :						str				=    self._logit(UpperCAmelCase_	,				UpperCAmelCase_	,				UpperCAmelCase_	,				self.out_projs[0]	)
                                  a_   :						Union[str, Any]				=    tf.nn.log_softmax(UpperCAmelCase_	)
                                  out.append(head_logprob[..., : self.cutoffs[0]]	)
                                  if target is not None:
                                         a_   :						Dict				=    tf.boolean_mask(UpperCAmelCase_	,				UpperCAmelCase_	)
                                         a_   :						Optional[int]				=    self._gather_logprob(UpperCAmelCase_	,				UpperCAmelCase_	)
                           else:
                                  a_   :						Optional[int]				=    self._logit(UpperCAmelCase_	,				UpperCAmelCase_	,				UpperCAmelCase_	,				self.out_projs[i]	)
                                  a_   :						List[Any]				=    tf.nn.log_softmax(UpperCAmelCase_	)
                                  a_   :						Union[str, Any]				=    self.cutoffs[0] + i - 1  # No probability for the head cluster
                                  a_   :						Union[str, Any]				=    head_logprob[..., cluster_prob_idx, None] + tail_logprob
                                  out.append(UpperCAmelCase_	)
                                  if target is not None:
                                         a_   :						Optional[int]				=    tf.boolean_mask(UpperCAmelCase_	,				UpperCAmelCase_	)
                                         a_   :						List[Any]				=    tf.boolean_mask(UpperCAmelCase_	,				UpperCAmelCase_	)
                                         a_   :						Any				=    self._gather_logprob(UpperCAmelCase_	,				UpperCAmelCase_	)
                                         cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
                           if target is not None:
                                  loss += tf.scatter_nd(UpperCAmelCase_	,				-cur_logprob	,				shape_list(UpperCAmelCase_	)	)
                    a_   :						Tuple				=    tf.concat(UpperCAmelCase_	,				axis=-1	)

             if target is not None:
                    if return_mean:
                           a_   :						Tuple				=    tf.reduce_mean(UpperCAmelCase_	)
                    # Add the training-time loss value to the layer using `self.add_loss()`.
                    self.add_loss(UpperCAmelCase_	)

                    # Log the loss as a metric (we could log arbitrary metrics,
                    # including different metrics for training and inference.
                    self.add_metric(UpperCAmelCase_	,				name=self.name	,				aggregation='mean' if return_mean else ''	)

             return out




"""simple docstring"""


import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
SCREAMING_SNAKE_CASE			  =  """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE			  =  direct_transformers_import(PATH_TO_TRANSFORMERS)

SCREAMING_SNAKE_CASE			  =  transformers.models.auto.configuration_auto.CONFIG_MAPPING

SCREAMING_SNAKE_CASE			  =  {
    # used to compute the property `self.chunk_length`
    """EncodecConfig""": ["""overlap"""],
    # used as `self.bert_model = BertModel(config, ...)`
    """DPRConfig""": True,
    # not used in modeling files, but it's an important information
    """FSMTConfig""": ["""langs"""],
    # used internally in the configuration class file
    """GPTNeoConfig""": ["""attention_types"""],
    # used internally in the configuration class file
    """EsmConfig""": ["""is_folding_model"""],
    # used during training (despite we don't have training script for these models yet)
    """Mask2FormerConfig""": ["""ignore_value"""],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    """OneFormerConfig""": ["""ignore_value""", """norm"""],
    # used during preprocessing and collation, see `collating_graphormer.py`
    """GraphormerConfig""": ["""spatial_pos_max"""],
    # used internally in the configuration class file
    """T5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    """MT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    """UMT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    # used internally in the configuration class file
    """LongT5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    """SwitchTransformersConfig""": ["""feed_forward_proj"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """BioGptConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """GLPNConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """SegformerConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """CvtConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """PerceiverConfig""": ["""layer_norm_eps"""],
    # used internally to calculate the feature size
    """InformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """TimeSeriesTransformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """AutoformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate `mlp_dim`
    """SamVisionConfig""": ["""mlp_ratio"""],
    # For (head) training, but so far not implemented
    """ClapAudioConfig""": ["""num_classes"""],
    # Not used, but providing useful information to users
    """SpeechT5HifiGanConfig""": ["""sampling_rate"""],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        """CLIPSegConfig""": True,
        """DeformableDetrConfig""": True,
        """DetaConfig""": True,
        """DinatConfig""": True,
        """DonutSwinConfig""": True,
        """EfficientFormerConfig""": True,
        """FSMTConfig""": True,
        """JukeboxConfig""": True,
        """LayoutLMv2Config""": True,
        """MaskFormerSwinConfig""": True,
        """MT5Config""": True,
        """NatConfig""": True,
        """OneFormerConfig""": True,
        """PerceiverConfig""": True,
        """RagConfig""": True,
        """SpeechT5Config""": True,
        """SwinConfig""": True,
        """Swin2SRConfig""": True,
        """Swinv2Config""": True,
        """SwitchTransformersConfig""": True,
        """TableTransformerConfig""": True,
        """TapasConfig""": True,
        """TransfoXLConfig""": True,
        """UniSpeechConfig""": True,
        """UniSpeechSatConfig""": True,
        """WavLMConfig""": True,
        """WhisperConfig""": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        """JukeboxPriorConfig""": True,
        # TODO: @Younes (for `is_decoder`)
        """Pix2StructTextConfig""": True,
    }
)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		False
       for attribute in attributes:
              for modeling_source in source_strings:
                     # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
                     if (
                         F"config.{attribute}" in modeling_source
                         or F"getattr(config, \"{attribute}\"" in modeling_source
                         or F"getattr(self.config, \"{attribute}\"" in modeling_source
                     ):
                            UpperCamelCase       			=		True
                     # Deal with multi-line cases
                     elif (
                         re.search(
                             RF"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\""		,   UpperCAmelCase_		,   )
                         is not None
                     ):
                            UpperCamelCase       			=		True
                     # `SequenceSummary` is called with `SequenceSummary(config)`
                     elif attribute in [
                         "summary_type",
                         "summary_use_proj",
                         "summary_activation",
                         "summary_last_dropout",
                         "summary_proj_to_labels",
                         "summary_first_dropout",
                     ]:
                            if "SequenceSummary" in modeling_source:
                                   UpperCamelCase       			=		True
                     if attribute_used:
                            break
              if attribute_used:
                     break

    # common and important attributes, even if they do not always appear in the modeling files
       UpperCamelCase       			=		[
           "bos_index",
           "eos_index",
           "pad_index",
           "unk_index",
           "mask_index",
           "image_size",
           "use_cache",
           "out_features",
           "out_indices",
       ]
       UpperCamelCase       			=		["encoder_no_repeat_ngram_size"]

       # Special cases to be allowed
       UpperCamelCase       			=		True
       if not attribute_used:
              UpperCamelCase       			=		False
              for attribute in attributes:
                     # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
                     if attribute in ["is_encoder_decoder"] and default_value is True:
                            UpperCamelCase       			=		True
                     elif attribute in ["tie_word_embeddings"] and default_value is False:
                            UpperCamelCase       			=		True

                     # Allow cases without checking the default value in the configuration class
                     elif attribute in attributes_to_allow + attributes_used_in_generation:
                            UpperCamelCase       			=		True
                     elif attribute.endswith("_token_id"					):
                            UpperCamelCase       			=		True

                     # configuration class specific cases
                     if not case_allowed:
                            UpperCamelCase       			=		SPECIAL_CASES_TO_ALLOW.get(config_class.__name__		,   []					)
                            UpperCamelCase       			=		allowed_cases is True or attribute in allowed_cases

       return attribute_used or case_allowed



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		dict(inspect.signature(config_class.__init__					).parameters					)
       UpperCamelCase       			=		[x for x in list(signature.keys()					) if x not in ["self", "kwargs"]]
       UpperCamelCase       			=		[signature[param].default for param in parameter_names]

       # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
       # as one variant is used, the test should pass
       UpperCamelCase       			=		{}
       if len(config_class.attribute_map					) > 0:
              UpperCamelCase       			=		{v: k for k, v in config_class.attribute_map.items()}

       # Get the path to modeling source files
       UpperCamelCase       			=		inspect.getsourcefile(UpperCAmelCase_					)
       UpperCamelCase       			=		os.path.dirname(UpperCAmelCase_					)
       # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
       UpperCamelCase       			=		[os.path.join(UpperCAmelCase_		,   UpperCAmelCase_					) for fn in os.listdir(UpperCAmelCase_					) if fn.startswith("modeling_"					)]

       # Get the source code strings
       UpperCamelCase       			=		[]
       for path in modeling_paths:
              if os.path.isfile(UpperCAmelCase_					):
                     with open(UpperCAmelCase_					) as fp:
                            modeling_sources.append(fp.read()					)

       UpperCamelCase       			=		[]
       for config_param, default_value in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
              # `attributes` here is all the variant names for `config_param`
              UpperCamelCase       			=		[config_param]
              # some configuration classes have non-empty `attribute_map`, and both names could be used in the
              # corresponding modeling files. As long as one of them appears, it is fine.
              if config_param in reversed_attribute_map:
                     attributes.append(reversed_attribute_map[config_param]					)

              if not check_attribute_being_used(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					):
                     unused_attributes.append(attributes[0]					)

       return sorted(UpperCAmelCase_					)



def 						lowerCamelCase__      (				)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		{}
       for _config_class in list(CONFIG_MAPPING.values()					):
              # Skip deprecated models
              if "models.deprecated" in _config_class.__module__:
                     continue
              # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
              UpperCamelCase       			=		[
                  cls
                  for name, cls in inspect.getmembers(
                      inspect.getmodule(_config_class					)		,   lambda UpperCAmelCase_					: inspect.isclass(UpperCAmelCase_					)
                      and issubclass(UpperCAmelCase_		,   UpperCAmelCase_					)
                      and inspect.getmodule(UpperCAmelCase_					) == inspect.getmodule(_config_class					)		,   )
              ]
              for config_class in config_classes_in_module:
                     UpperCamelCase       			=		check_config_attributes_being_used(UpperCAmelCase_					)
                     if len(UpperCAmelCase_					) > 0:
                            UpperCamelCase       			=		unused_attributes

       if len(UpperCAmelCase_					) > 0:
              UpperCamelCase       			=		"The following configuration classes contain unused attributes in the corresponding modeling files:\n"
              for name, attributes in configs_with_unused_attributes.items():
                     error += F"{name}: {attributes}\n"

              raise ValueError(UpperCAmelCase_					)


if __name__ == "__main__":
      check_config_attributes()





